Update README.md

README.md

@@ -71,6 +71,7 @@ Evaluation results on the test set:
 ## Usage
 
 ```python
+import torch
 from transformers import AutoModelForQuestionAnswering, AutoTokenizer
 
 # Load model and tokenizer
@@ -78,8 +79,8 @@ model = AutoModelForQuestionAnswering.from_pretrained("real-jiakai/bert-base-chi
 tokenizer = AutoTokenizer.from_pretrained("real-jiakai/bert-base-chinese-finetuned-cmrc2018")
 
 # Prepare inputs
-question = "Your question in Chinese"
-context = "Context text in Chinese"
+question = "长城有多长？"
+context = "长城是中国古代的伟大建筑工程，全长超过2万公里，横跨中国北部多个省份。"
 
 # Tokenize inputs
 inputs = tokenizer(
@@ -87,14 +88,24 @@ inputs = tokenizer(
     context,
     return_tensors="pt",
     max_length=384,
-    truncation=True,
-    return_offsets_mapping=True
+    truncation=True
+)
+
+# Tokenize inputs
+inputs = tokenizer(
+    question,
+    context,
+    return_tensors="pt",
+    max_length=384,
+    truncation=True
 )
 
 # Get answer
 outputs = model(**inputs)
-start_logits = outputs.start_logits
-end_logits = outputs.end_logits
+answer_start = torch.argmax(outputs.start_logits)
+answer_end = torch.argmax(outputs.end_logits) + 1
+answer = tokenizer.decode(inputs["input_ids"][0][answer_start:answer_end])
+print("Answer:", answer)
 ```
 
 ## Citation
@@ -121,5 +132,4 @@ If you use this model, please cite the CMRC2018 dataset:
     doi = "10.18653/v1/D19-1600",
     pages = "5886--5891",
 }
-
 ```